Convex Analysis and Optimization

Professor Michael Friedlander, University of British Columbia

Term 2, 2016–2017
Monday and Wednesday, 9:00–10:30am in DMP 101.

Convex optimization is essential to a range of current scientific and engineering applications, including machine learning, signal processing, and control systems. It is also forms the backbone for other areas of optimization. The aim of this course is to provide a self-contained introduction to basic concepts in convex analysis and its use in convex optimization.

This course is cross-listed as both CS542F (Topics in Numerical Computation) and MATH 604 (Topics in Optimization).


This list represents a tentative outline of the topics that will be covered.

Part 1: Convex sets

Part 2: Convex functions

Part 3: Convex optimization

Target audience

This course is intended for students who wish to learn the underpinnings of convex optimization and are considering research in the area. Students looking to gain more practical experience with optimization (e.g., how to use various solvers) may wish to instead consider CPSC 406, which will be taught next year.


Background in vector calculus, linear algebra, and basic real analysis.


Auditors and undergraduates

Auditors are welcome. Graduate students who wish to audit, please bring a graduate registration form to the first lecture. Undergraduate students who wish to take the course for credit should fill out an undergraduate registration form.


The course isn’t based on any one particular text. These references should be helpful for further reading.


Sign up for the Piazza course page.